Journal of Machine Learning Research最新文献_第10页

CVXPY: A Python-Embedded Modeling Language for Convex Optimization. 一种用于凸优化的python嵌入式建模语言。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-04-01

Steven Diamond, Stephen Boyd

引用次数: 0

A Gibbs Sampler for Learning DAGs. 学习 DAG 的 Gibbs 采样器

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-04-01

Robert J B Goudie, Sach Mukherjee

引用次数: 0

On Quantile Regression in Reproducing Kernel Hilbert Spaces with Data Sparsity Constraint. 数据稀疏性约束下核希尔伯特空间再现的分位数回归。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-04-01

Chong Zhang, Yufeng Liu, Yichao Wu

{"title":"On Quantile Regression in Reproducing Kernel Hilbert Spaces with Data Sparsity Constraint.","authors":"Chong Zhang, Yufeng Liu, Yichao Wu","doi":"","DOIUrl":"","url":null,"abstract":"For spline regressions, it is well known that the choice of knots is crucial for the performance of the estimator. As a general learning framework covering the smoothing splines, learning in a Reproducing Kernel Hilbert Space (RKHS) has a similar issue. However, the selection of training data points for kernel functions in the RKHS representation has not been carefully studied in the literature. In this paper we study quantile regression as an example of learning in a RKHS. In this case, the regular squared norm penalty does not perform training data selection. We propose a data sparsity constraint that imposes thresholding on the kernel function coefficients to achieve a sparse kernel function representation. We demonstrate that the proposed data sparsity method can have competitive prediction performance for certain situations, and have comparable performance in other cases compared to that of the traditional squared norm penalty. Therefore, the data sparsity method can serve as a competitive alternative to the squared norm penalty method. Some theoretical properties of our proposed method using the data sparsity constraint are obtained. Both simulated and real data sets are used to demonstrate the usefulness of our data sparsity constraint.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4850041/pdf/nihms729829.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34446126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiplicative Multitask Feature Learning. 乘法多任务特征学习

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-04-01

Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun, Minghu Song

{"title":"Multiplicative Multitask Feature Learning.","authors":"Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun, Minghu Song","doi":"","DOIUrl":"","url":null,"abstract":"We investigate a general framework of multiplicative multitask feature learning which decomposes individual task's model parameters into a multiplication of two components. One of the components is used across all tasks and the other component is task-specific. Several previous methods can be proved to be special cases of our framework. We study the theoretical properties of this framework when different regularization conditions are applied to the two decomposed components. We prove that this framework is mathematically equivalent to the widely used multitask feature learning methods that are based on a joint regularization of all model parameters, but with a more general form of regularizers. Further, an analytical formula is derived for the across-task component as related to the task-specific component for all these regularizers, leading to a better understanding of the shrinkage effects of different regularizers. Study of this framework motivates new multitask learning algorithms. We propose two new learning formulations by varying the parameters in the proposed framework. An efficient blockwise coordinate descent algorithm is developed suitable for solving the entire family of formulations with rigorous convergence analysis. Simulation studies have identified the statistical properties of data that would be in favor of the new formulations. Extensive empirical studies on various classification and regression benchmark data sets have revealed the relative advantages of the two new formulations by comparing with the state of the art, which provides instructive insights into the feature learning problem with multiple tasks.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5395291/pdf/nihms814714.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34930505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-corpora unsupervised learning of trajectories in autism spectrum disorders 自闭症谱系障碍轨迹的跨语料库无监督学习

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01 DOI: 10.5555/2946645.3007086

ElibolHuseyin Melih, NguyenVincent, LindermanScott, JohnsonMatthew, HashmiAmna, Doshi-VelezFinale

引用次数: 0

Guarding against Spurious Discoveries in High Dimensions. 在高维中防范虚假的发现。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01

Jianqing Fan, Wen-Xin Zhou

{"title":"Guarding against Spurious Discoveries in High Dimensions.","authors":"Jianqing Fan, Wen-Xin Zhou","doi":"","DOIUrl":"","url":null,"abstract":"Many data-mining and statistical machine learning algorithms have been developed to select a subset of covariates to associate with a response variable. Spurious discoveries can easily arise in high-dimensional data analysis due to enormous possibilities of such selections. How can we know statistically our discoveries better than those by chance? In this paper, we define a measure of goodness of spurious fit, which shows how good a response variable can be fitted by an optimally selected subset of covariates under the null model, and propose a simple and effective LAMM algorithm to compute it. It coincides with the maximum spurious correlation for linear models and can be regarded as a generalized maximum spurious correlation. We derive the asymptotic distribution of such goodness of spurious fit for generalized linear models and L1-regression. Such an asymptotic distribution depends on the sample size, ambient dimension, the number of variables used in the fit, and the covariance information. It can be consistently estimated by multiplier bootstrapping and used as a benchmark to guard against spurious discoveries. It can also be applied to model selection, which considers only candidate models with goodness of fits better than those by spurious fits. The theory and method are convincingly illustrated by simulated examples and an application to the binary outcomes from German Neuroblastoma Trials.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5603346/pdf/nihms842539.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35535102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structure discovery in Bayesian networks by sampling partial orders 抽样偏阶贝叶斯网络的结构发现

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01 DOI: 10.5555/2946645.2946702

NiinimäkiTeppo, ParviainenPekka, KoivistoMikko

引用次数: 1

Choice of V for V-fold cross-validation in least-squares density estimation 最小二乘密度估计中V-fold交叉验证V值的选择

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01 DOI: 10.5555/2946645.3053490

ArlotSylvain, LerasleMatthieu

引用次数: 0

MOCCA: Mirrored Convex/Concave Optimization for Nonconvex Composite Functions. 非凸复合函数的镜像凸/凹优化。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01

Rina Foygel Barber, Emil Y Sidky

{"title":"MOCCA: Mirrored Convex/Concave Optimization for Nonconvex Composite Functions.","authors":"Rina Foygel Barber, Emil Y Sidky","doi":"","DOIUrl":"","url":null,"abstract":"Many optimization problems arising in high-dimensional statistics decompose naturally into a sum of several terms, where the individual terms are relatively simple but the composite objective function can only be optimized with iterative algorithms. In this paper, we are interested in optimization problems of the form F(Kx) + G(x), where K is a fixed linear transformation, while F and G are functions that may be nonconvex and/or nondifferentiable. In particular, if either of the terms are nonconvex, existing alternating minimization techniques may fail to converge; other types of existing approaches may instead be unable to handle nondifferentiability. We propose the MOCCA (mirrored convex/concave) algorithm, a primal/dual optimization approach that takes a local convex approximation to each term at every iteration. Inspired by optimization problems arising in computed tomography (CT) imaging, this algorithm can handle a range of nonconvex composite optimization problems, and offers theoretical guarantees for convergence when the overall problem is approximately convex (that is, any concavity in one term is balanced out by convexity in the other term). Empirical results show fast convergence for several structured signal recovery problems.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5789814/pdf/nihms870482.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35785739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes. 支持向量危险机：一个计算过程框架，用于学习审查结果的风险评分。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-01-01 Epub Date: 2016-08-01

Yuanjia Wang, Tianle Chen, Donglin Zeng

{"title":"Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes.","authors":"Yuanjia Wang, Tianle Chen, Donglin Zeng","doi":"","DOIUrl":"","url":null,"abstract":"Learning risk scores to predict dichotomous or continuous outcomes using machine learning approaches has been studied extensively. However, how to learn risk scores for time-to-event outcomes subject to right censoring has received little attention until recently. Existing approaches rely on inverse probability weighting or rank-based regression, which may be inefficient. In this paper, we develop a new support vector hazards machine (SVHM) approach to predict censored outcomes. Our method is based on predicting the counting process associated with the time-to-event outcomes among subjects at risk via a series of support vector machines. Introducing counting processes to represent time-to-event data leads to a connection between support vector machines in supervised learning and hazards regression in standard survival analysis. To account for different at risk populations at observed event times, a time-varying offset is used in estimating risk scores. The resulting optimization is a convex quadratic programming problem that can easily incorporate non-linearity using kernel trick. We demonstrate an interesting link from the profiled empirical risk function of SVHM to the Cox partial likelihood. We then formally show that SVHM is optimal in discriminating covariate-specific hazard function from population average hazard function, and establish the consistency and learning rate of the predicted risk using the estimated risk scores. Simulation studies show improved prediction accuracy of the event times using SVHM compared to existing machine learning methods and standard conventional approaches. Finally, we analyze two real world biomedical study data where we use clinical markers and neuroimaging biomarkers to predict age-at-onset of a disease, and demonstrate superiority of SVHM in distinguishing high risk versus low risk subjects.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5210213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71434774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0